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Although useful, ADNC diagnostic frameworks have limitations, particularly in advanced disease stages where pathological severity varies widely within a given diagnostic category. Further, some individuals lacking cognitive impairment are inappropriately categorized as having severe ADNC. In this study, quantitative pathology metrics and alternative tissue sampling schemes were integrated with data about premortem cognitive status, in order to derive clinically informed neuropathologic diagnostic thresholds. Specific goals of the current study were to generate data\u2010driven, standardized diagnostic cut\u2010points, with the most severe stages of ADNC having consistent implications: Braak neurofibrillary tangle (NFT) stage V cases being always impaired (MCI or demented) and Braak NFT stage VI cases being always demented. Utilizing whole\u2010slide imaging and AI\u2010based image analysis, object\u2010based (NFT counts) and pixel\u2010based (phosphorylated tau [pTau] burden) quantifications were compared across neocortical regions in three subsamples of cases from the University of Kentucky ADRC autopsy cohort (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009329, all with clinical evaluations within 2 years of death). We also compared between HALO\u2010 and Aperio\u2010based platform results, and between AT8 and PHF\u20101 pTau antibodies. Applying refined thresholds enabled reclassification of cases previously misaligned with their digitally determined appropriate status: 17% of cases were thus reclassified. The use of commercially available software, standardized classifier architectures, and interoperable analysis pipelines facilitated scalable and reproducible digital quantification. Cross\u2010institutional validation at University of Texas San Antonio, with the same algorithms applied in both research centers, confirmed near\u2010perfect agreement of pathology counts, underscoring shareability and the feasibility of harmonized digital workflows for collaborative research and diagnostic purposes. These findings support the integration of quantitative digital pathology into standard neuropathological protocols and provide a scalable model for future multi\u2010site studies. Enabling comparisons of analytical platforms, pTau antibodies, and anatomical sampling strategies, an updated workflow demonstrated high reproducibility and consistent clinical\u2010pathological correlations.\n                  <\/jats:p>","DOI":"10.1111\/bpa.70102","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T22:05:22Z","timestamp":1777241122000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data\u2010driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology"],"prefix":"10.1111","volume":"36","author":[{"given":"Ryan K.","family":"Shahidehpour","sequence":"first","affiliation":[{"name":"Sanders\u2010Brown Center on Aging University of Kentucky  Lexington Kentucky USA"},{"name":"Department of Neuroscience University of Kentucky  Lexington Kentucky 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